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# Quality Filtering

## Docs

- [Preprocessing Stages](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/quality-filtering/preprocessing.md): Audio preprocessing stages — mono conversion, segment concatenation, and timestamp mapping for downstream filtering pipelines
- [VAD Segmentation](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/quality-filtering/vad.md): Voice activity detection with VADSegmentationStage to split audio into speech segments using Silero VAD
- [Band Filter](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/quality-filtering/band-filter.md): Filter audio by spectral bandwidth — full-band vs narrow-band — using BandFilterStage and a scikit-learn classifier
- [UTMOS Filter](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/quality-filtering/utmos.md): Filter audio segments by predicted Mean Opinion Score using UTMOSFilterStage and the utmos22_strong model
- [SIGMOS Filter](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/quality-filtering/sigmos.md): Filter audio across seven independent perceptual quality dimensions — noise, overall, signal, coloration, discontinuity, loudness, reverb — using SIGMOSFilterStage
- [Speaker Separation](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/quality-filtering/speaker-separation.md): Diarize audio with offline SortFormer or streaming Sortformer and fan out one task per speaker for downstream per-speaker filtering
- [AudioDataFilterStage Composite](https://docs.nvidia.com/nemo/curator/v26.04/curate-audio/process-data/quality-filtering/audio-data-filter-stage.md): AudioDataFilterStage composite — end-to-end audio curation pipeline that decomposes into VAD, band, UTMOS, SIGMOS, and speaker separation sub-stages from a single YAML config


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> Compose preprocessing, VAD, band, MOS scoring, and speaker separation stages to extract clean single-speaker training segments from raw audio

# Audio Quality Filtering

Curate raw audio into clean, single-speaker training segments using a configurable suite of preprocessing, segmentation, and filtering stages. Use these stages individually for targeted curation, or compose them through `AudioDataFilterStage` for an end-to-end pipeline driven by a single YAML config.

## How it Works

A typical pipeline composes the following stages in order:

1. **Mono conversion** normalizes channels and sample rate.
2. **Voice activity detection (VAD)** splits each file into speech segments.
3. **Band filter** drops segments that are not full-band (or not narrow-band, depending on the configured target).
4. **UTMOS** filters segments below a perceived-quality threshold.
5. **SIGMOS** filters segments by per-dimension quality scores (noise, overall, signal, coloration, discontinuity, loudness, reverb).
6. **Segment concatenation** merges surviving segments back together with configurable silence between them.
7. **Speaker separation** diarizes the concatenated audio and fans out one task per speaker.
8. **Per-speaker filters** rerun VAD/Band/UTMOS/SIGMOS on each speaker's audio independently.
9. **Timestamp mapping** projects final segment boundaries back to positions in the original input file.

Each stage is independently usable. Use [`AudioDataFilterStage`](/curate-audio/process-data/quality-filtering/audio-data-filter-stage) to compose all of them with a single YAML config, or assemble a custom subset stage-by-stage.

## Pipeline Stages

#### [Preprocessing Stages](/curate-audio/process-data/quality-filtering/preprocessing)

Channel normalization, segment merging, and original-file timestamp mapping
mono-conversion
concatenation
timestamp-mapper

#### [VAD Segmentation](/curate-audio/process-data/quality-filtering/vad)

Split audio into speech segments using Silero VAD
silero
fan-out
configurable

#### [Band Filter](/curate-audio/process-data/quality-filtering/band-filter)

Classify and filter audio by spectral bandwidth
full-band
narrow-band
sklearn

#### [UTMOS Filter](/curate-audio/process-data/quality-filtering/utmos)

Filter by predicted Mean Opinion Score using utmos22\_strong
mos
torch-hub
no-reference

#### [SIGMOS Filter](/curate-audio/process-data/quality-filtering/sigmos)

Filter by seven independent perceptual-quality dimensions
onnx
multi-dimensional
configurable

#### [Speaker Separation](/curate-audio/process-data/quality-filtering/speaker-separation)

Diarize with offline or streaming SortFormer and fan out per speaker
sortformer
streaming
diarization

#### [AudioDataFilterStage Composite](/curate-audio/process-data/quality-filtering/audio-data-filter-stage)

Single composite stage that decomposes into the full filtering pipeline from a YAML config
composite
yaml-config
end-to-end

## Quick Example

A complete VAD + UTMOS + SIGMOS + speaker separation pipeline assembled stage-by-stage:

```python
from nemo_curator.pipeline import Pipeline
from nemo_curator.backends.xenna import XennaExecutor
from nemo_curator.stages.audio.preprocessing.mono_conversion import MonoConversionStage
from nemo_curator.stages.audio.segmentation.vad_segmentation import VADSegmentationStage
from nemo_curator.stages.audio.filtering.utmos import UTMOSFilterStage
from nemo_curator.stages.audio.filtering.sigmos import SIGMOSFilterStage
from nemo_curator.stages.audio.preprocessing.concatenation import SegmentConcatenationStage
from nemo_curator.stages.audio.segmentation.speaker_separation import SpeakerSeparationStage
from nemo_curator.stages.audio.postprocessing.timestamp_mapper import TimestampMapperStage
from nemo_curator.stages.audio.io.convert import AudioToDocumentStage
from nemo_curator.stages.text.io.writer import JsonlWriter

pipeline = Pipeline(name="audio_quality_filtering")

# 1. Normalize channels and sample rate
pipeline.add_stage(MonoConversionStage(output_sample_rate=48000))

# 2. Split into speech segments
pipeline.add_stage(VADSegmentationStage(min_duration_sec=2.0, threshold=0.5))

# 3. Filter by perceptual quality (drop segments with MOS < 3.5)
pipeline.add_stage(UTMOSFilterStage(mos_threshold=3.5))

# 4. Filter by SIGMOS noise + overall thresholds
pipeline.add_stage(SIGMOSFilterStage(noise_threshold=4.0, ovrl_threshold=3.5))

# 5. Concatenate surviving segments
pipeline.add_stage(SegmentConcatenationStage(silence_duration_sec=0.5))

# 6. Diarize and fan out per speaker
pipeline.add_stage(SpeakerSeparationStage())

# 7. Map final boundaries back to original file timestamps
pipeline.add_stage(TimestampMapperStage())

# 8. Export
pipeline.add_stage(AudioToDocumentStage())
pipeline.add_stage(JsonlWriter(path="./curated_audio"))

executor = XennaExecutor()
pipeline.run(executor)
```

For a YAML-driven equivalent, use [`AudioDataFilterStage`](/curate-audio/process-data/quality-filtering/audio-data-filter-stage) — it expands into the same pipeline from a single configuration file.

## Related Topics

* **[ReadSpeech Tutorial](/curate-audio/tutorials/readspeech)** — end-to-end walkthrough of `AudioDataFilterStage` on the DNS Challenge ReadSpeech dataset.
* **[Quality Assessment](/curate-audio/process-data/quality-assessment)** — WER and duration filters for ASR-based curation.
* **[Audio Concepts](/about/concepts/audio)** — audio task model, manifests, and pipeline architecture.